code: def test_from_partition_data(tmp_path):
      data = FakeDataset()[0]
      num_parts = 2
  
      partitioner = Partitioner(data, num_parts, tmp_path)
      partitioner.generate_partition()
  
      graph_store1 = LocalGraphStore.from_partition(tmp_path, pid=0)
      graph_store2 = LocalGraphStore.from_partition(tmp_path, pid=1)
  
      attr1 = graph_store1.get_all_edge_attrs()[0]
      (row1, col1) = graph_store1.get_edge_index(attr1)
      attr2 = graph_store2.get_all_edge_attrs()[0]
      (row2, col2) = graph_store2.get_edge_index(attr2)
      assert row1.size(0) + row2.size(0) == data.num_edges
  
      feat_store1 = LocalFeatureStore.from_partition(tmp_path, pid=0)
      feat_store2 = LocalFeatureStore.from_partition(tmp_path, pid=1)
  
      node_attr1 = feat_store1.get_all_tensor_attrs()[0]
      assert node_attr1.attr_name == "x"
      x1 = feat_store1.get_tensor(node_attr1)
      id1 = feat_store1.get_global_id(node_attr1.group_name)
  
      node_attr2 = feat_store2.get_all_tensor_attrs()[0]
      assert node_attr2.attr_name == "x"
      x2 = feat_store2.get_tensor(node_attr2)
      id2 = feat_store2.get_global_id(node_attr2.group_name)
  
      assert x1.size(0) + x2.size(0) == data.num_nodes
      assert torch.allclose(data.x[id1], x1)
      assert torch.allclose(data.x[id2], x2)
  
file_path: pytorch_geometric/test/distributed/test_partition.py
imports: import os.path
  import pytest
  import torch
  from torch_geometric.datasets import FakeDataset, FakeHeteroDataset
  from torch_geometric.distributed import LocalFeatureStore, LocalGraphStore, Partitioner
  from torch_geometric.typing import EdgeTypeStr
label_desc: Use image node_id 3 for calling a global function with example usage: test_from_partition_data(tmp_path) without return types